Voice morphing apparatus having adjustable parameters

ABSTRACT

A voice morphing apparatus having adjustable parameters is described. The disclosed system and method include a voice morphing apparatus that morphs input audio to mask a speaker&#39;s identity. Parameter adjustment uses evaluation of an objective function that is based on the input audio and output of the voice morphing apparatus. The voice morphing apparatus includes objectives that are based adversarially on speaker identification and positively on audio fidelity. Thus, the voice morphing apparatus is adjusted to reduce identifiability of speakers while maintaining fidelity of the morphed audio. The voice morphing apparatus may be used as part of an automatic speech recognition system.

FIELD

The invention is related to and in the field of audio processing devicesand, more specifically, to an apparatus for morphing a human voice, ofwhich some embodiments are relate to training a voice morphing apparatusand some embodiments are used in the field of speech processing.

BACKGROUND

Recent advances in computing have raised the possibility of realizingmany long sought-after voice-control applications. For example,improvements in statistical models, including practical frameworks foreffective neural network architectures, have greatly increased theaccuracy and reliability of previous speech processing systems. This hasbeen coupled with a rise in wide area computer networks, which offer arange of modular services that can be simply accessed using applicationprogramming interfaces. Voice is quickly becoming a viable option forproviding a user interface.

However, voice has a disadvantage when compared to text or othergraphical input methods, namely that it is often easy to identify aparticular speaker from captured speech. In many cases, it may bedesired to use voice as an input interface but avoid a possibility ofidentifying the speaker. For example, a user may wish to make a voiceenquiry without being identified and/or tracked. As a comparison, webbrowsers provide a private browsing or “incognito” mode that limits anamount of personal information that is exchanged with Internet servers.It would be useful to allow a similar mode for voice input. Voiceanonymity may also be useful for allowing the exchange of voice data totrain large linguistic neural network models. Often supervised learningmodels require labelled data, which involves manually labelling voicesamples. It would be advantageous to anonymize voice data before it issent to labelers.

Fahimeh Bahmaninezhad et al. in the paper “Convolutional Neural NetworkBased Speaker De-Identification” presented at Odyssey 2018, The Speakerand Language Recognition Workshop in Les Sables d'Olonne, France (thecontents of which are incorporated herein by reference), describe amethod of concealing speaker identity in speech signals. The proposedspeaker de-identification system maps a voice of a given speaker to anaverage (or gender-dependent average) voice. The mapping is modeled by anew convolutional neural network (CNN) encoder-decoder architecture. Themethod is tested on the voice conversion challenge 2016 (VCC-2016)database.

Providing speaker de-identification and voice anonymity is difficult.Many existing systems seek to map a source speaker onto a targetspeaker, or an average of target speakers. However, it is easy fortrained neural network systems to produce unintelligible or heavilydistorted outputs that destroy the information carried in the voicesignal. Additionally, comparative systems such as that proposed byFahimeh Bahmaninezhad map distinctive characteristics of input speech todifferent but still distinctive characteristics in the output speech,allowing some form of identification. It is also difficult tode-identify a speaker yet maintain non-identifying characteristics ofspeech audio such as noise, gender and accent.

Therefore, what is needed are systems and methods for voice modificationto allow for user anonymity and privacy.

SUMMARY

Aspects and embodiments of the invention are set out in the independentclaim(s).

In accordance with various aspects and embodiments of the presentinvention, there is provided a method of training a voice morphingapparatus. The method includes evaluating an objective function for aplurality of data samples, each data sample including an input for thevoice morphing apparatus, the objective function being defined as afunction of at least an output of the voice morphing apparatus, theobjective function including: a first term based on speakeridentification, the first term modifying the objective functionproportional to a measure of speaker identification based on at leastthe output of the voice morphing apparatus; and a second term based onaudio fidelity of at least the output of the voice morphing apparatus,the second term modifying the objective function proportional to ameasure of audio fidelity between the output and the input of the voicemorphing apparatus. The method further includes adjusting parameters ofthe voice morphing apparatus based on the evaluating.

In accordance with some aspects and embodiments of the invention, bytraining a voice morphing apparatus using input audio data, e.g.unlabeled voice samples, and terms that are in opposition, a certaintyof speaker identification may be reduced, effectively masking aspeaker's identity while maintaining an audio fidelity, e.g. maintainingaudio data that sounds like speech and may be processed by conventionalspeech processing systems. The objective function may include a lossfunction, in which case the first term may increase the loss based on acertainty or confidence of speaker identification and the second termmay decrease the loss based on a similarity of the input and output.

In accordance with various aspects, the voice morphing apparatusincludes an artificial neural network architecture and adjustingparameters of the voice morphing apparatus includes applying a gradientdescent method to a derivative of the objective function with respect tothe parameters of the artificial neural network architecture. Theseaspects may thus be implemented using standardized neural networksoftware libraries that provide for custom loss functions.

In accordance with various aspects, the second term is computed using anoutput of an audio processing component of an automatic speechrecognition system. The audio processing component may be used tocompute a speaker intelligibility measure for the second term, e.g. bycomputing a first phoneme recognition score for the input to the voicemorphing apparatus using the audio processing component; computing asecond phoneme recognition score for the output from the voice morphingapparatus using the audio processing component; and computing the secondterm for the objective function based on a comparison between the firstand second phoneme recognition scores. Re-using existing components ofan automatic speech recognition system may allow for easy implementationand also ensures that the voice morphing apparatus is trainedconsistently with speech processing functions that may be applied to anoutput of the apparatus. In this case, it may be ensured that the voicemorphing apparatus does not overly degrade the accuracy of acousticmodels that may be applied to morphed voices.

In accordance with various aspects, the method comprises comparing aspectrogram for the input to the voice morphing apparatus and aspectrogram for the output of the voice morphing apparatus; andcomputing the second term for the objective function based on thecomparison. This may ensure that audio features are suitably conserveddespite the voice being morphed, e.g. such that the audio still sounds“voice-like” and maintains similar-sounding transient and constantnoise.

In accordance with various aspects, the first term is computed using anoutput of a speaker identification component of an automatic speechrecognition system. The first term is based on a certainty score outputby the speaker identification component. In certain cases, the firstterm may be computed by computing a first speaker identification vectorfor the input to the voice morphing apparatus using the speakeridentification component; computing a second speaker identificationvector for the output from the voice morphing apparatus using thespeaker identification component; and comparing the first and secondspeaker identification vectors. Again, using existing speech processingcomponents reduces the implementational complexity. Comparing an outputof parallel speaker identification processes may provide one way ofmeasuring a change in speaker identification ability.

In accordance with various aspects, the objective function comprises oneor more further terms based on one or more of: a gender classificationusing at least the output of the voice morphing apparatus; and an accentclassification using at least the output of the voice morphingapparatus, wherein the one or more further terms are weighted to eithermaintain or move away from one or more of a gender classification and anaccent classification. In one aspect, one of more classifiers may beused to determine one or more further terms that allow for certaincharacteristics of a voice to be maintained despite a masking of thespeaker identify. For example, applying gender and accent classifiersmay allow for gender and accent to be maintained. In certain aspects theone or more further terms are based on a comparative score between aclassification applied to the input of the voice morphing apparatus anda classification applied to the output of the voice morphing apparatusand input data is pre-selected to provide a defined distribution ofvoice characteristics.

In accordance with one aspect, there is provided a system for training avoice morphing apparatus, the system comprising a voice morphingapparatus configured to evaluate an objective function for a pluralityof data samples, each data sample comprising an input for the voicemorphing apparatus, the objective function being defined as a functionof at least an output of the voice morphing apparatus. The objectivefunction comprises a first term based on speaker identification, thefirst term modifying the objective function proportional to a measure ofspeaker identification based on at least the output of the voicemorphing apparatus and a second term based on audio fidelity of at leastthe output of the voice morphing apparatus, the second term modifyingthe objective function proportional to a measure of audio fidelitybetween the output and the input of the voice morphing apparatus. Thesystem being further configured to adjust the parameters based on theevaluating.

The voice morphing apparatus may comprise an artificial neural networkarchitecture. The system (for example an objective function evaluator)may adjust the parameters by applying a gradient descent method to aderivative of the objective function with respect to the parameters ofthe artificial neural network architecture.

The system may further comprise an automatic speech recognition systemcomprising an audio processing component. The system may compute thesecond term using an output of the audio processing component. Thesystem may compute a speaker intelligibility measure for the second termusing the audio processing component.

The audio processing component may compute a first phoneme recognitionscore for the input to the voice morphing apparatus and a second phonemerecognition score for the output from the voice morphing apparatus. Thesystem may compute the second term for the objective function based on acomparison between the first and second phoneme recognition scores.

The system may compare a spectrogram for the input to the voice morphingapparatus and a spectrogram for the output of the voice morphingapparatus and compute the second term for the objective function basedon the comparison.

The system may comprise a speaker identification component. The systemmay compute the first term using an output of a speaker identificationcomponent. The speaker identification component may output a certaintyscore. The first term may be based on the certainty score output by thespeaker identification component.

The speaker identification component may be used to compute a firstspeaker identification vector for the input to the voice morphingapparatus. The speaker identification component may be sued to compute asecond speaker identification vector for the output from the voicemorphing apparatus. The system may compute the first term for theobjective function based on a comparison between the first and secondspeaker identification vectors.

The voice morphing apparatus may be configured to evaluate the objectivefunction further comprising a gender classification using at least theoutput of the voice morphing apparatus and an accent classificationusing at least the output of the voice morphing apparatus, wherein theone or more further terms are weighted to either maintain or move awayfrom one or more of a gender classification and an accentclassification.

The system may apply a classification to the input of the voice morphingapparatus. The system may apply a classification to the output of thevoice morphing apparatus. The one or more further terms may be based ona comparative score between the classification applied to the input ofthe voice morphing apparatus and the classification applied to theoutput of the voice morphing apparatus.

The system may pre-select input data to provide a defined distributionof voice characteristics.

In accordance with another aspect, a system for training a voicemorphing apparatus is provided. The system comprises a voice morphingapparatus comprising a set of trainable parameters, the voice morphingapparatus being configured to map input audio data to output audio data;a speaker identification system configured to output speakeridentification data based on input audio data; and an audio fidelitysystem configurated to output audio fidelity data. The system isconfigured to pass at least output audio data for the voice morphingapparatus to the speaker identification system and the audio fidelitysystem, wherein the system is configured to train the voice morphingapparatus using at least a set of input audio data, and wherein anoutput of the speaker identification system and an output of the audiofidelity system are used by the system to adjust the set of trainableparameters.

This system may provide benefits similar to the above-mentioned method.The voice morphing apparatus may comprise an artificial neural networkarchitecture.

In accordance with various aspects, the speaker identification system isconfigured to output a score indicative of a confidence ofidentification for one or more speakers, and wherein the system isconfigured to evaluate an objective function with a first term based onthe score indicative of a confidence of identification, the objectivefunction causing the system to adjust the set of trainable parameters toreduce the score. The speaker identification system may comprise aspeaker identification component and the system may be configured totrain the voice morphing apparatus to maximize a difference betweenoutputs of the speaker identification component for the input audio dataand the output audio data of the voice morphing apparatus. Speakeridentification systems may be configured to output confidence orprobability data as part of a prediction; this data may thus be re-usedto train the voice morphing apparatus.

In accordance with various aspects, the audio fidelity system comprisesa speaker intelligibility component, the speaker intelligibilitycomponent comprising a speech processing component. The speakerintelligibility component may comprise a phoneme recognition componentand the audio fidelity system may be configured to output a measure ofsimilarity based on a difference between outputs of the phonemerecognition component for the input audio data and the output audio dataof the voice morphing apparatus, wherein the system is configured totrain the voice morphing apparatus to minimize said difference. In thiscase, existing front-end components of an automatic speech recognitionsystem may be re-purposed to train the voice morphing apparatus tomaintain an intelligibility of morphed speech. The audio fidelity systemmay further comprise an audio similarity component configured to comparethe input audio data and the output audio data of the voice morphingapparatus, wherein the audio fidelity system may be configured to outputa measure of similarity based on an output of the audio similaritycomponent, the system being configured to train the voice morphingapparatus to maximize an output of the audio similarity component forthe input audio data and the output audio data. The audio similaritycomponent may be configured to generate a score indicative of aspectrogram similarity. This may help train the voice morphing apparatusto morph speech in a manner that retains speech or voice-like audiocharacteristics, despite a masking of the speaker identity.

In accordance with various aspects, the system comprises one or morevoice feature classifiers, wherein the system is configured to apply theone or more voice feature classifiers to at least the output audio datafor the voice morphing apparatus and to use an output of the one or morevoice feature classifiers to adjust the set of trainable parameters forthe voice morphing apparatus. These voice feature classifiers may beused as part of an objective or loss function for the training of thevoice morphing apparatus to retain or discard (depending onconfiguration) certain aspects of speech such as gender or accent. Thesystem may be configured to compare outputs of the one or more voicefeature classifiers for the input audio data and the output audio dataof the voice morphing apparatus and to use an output of the comparisonto adjust the set of trainable parameters for the voice morphingapparatus.

In accordance with another aspect, a method of training a voice morphingapparatus is provided. The method comprises mapping, by a voice morphingapparatus comprising a set of trainable parameters, input audio data tooutput audio data, outputting, by a speaker identification system,speaker identification data based on input audio data, and outputting,by an audio fidelity system, an audio fidelity data, passing at leastoutput audio data for the voice morphing apparatus to the speakeridentification system and the audio fidelity system, training the voicemorphing apparatus using at least a set of input audio data, and usingan output of the speaker identification system and an output of theaudio fidelity system to adjust the set of trainable parameters.

The method may comprise outputting a score indicative of a confidence ofidentification for one or more speakers, and evaluating an objectivefunction with a first term based on the score indicative of a confidenceof identification, and adjusting, using the objective function, the setof trainable parameters to reduce the score.

The speaker identification system may comprise a speaker identificationcomponent. The method may comprise training the voice morphing apparatusto maximize a difference between outputs of the speaker identificationcomponent for the input audio data and the output audio data of thevoice morphing apparatus.

The audio fidelity system may comprise a speaker intelligibilitycomponent, the speaker intelligibility component may comprise a speechprocessing component. The speaker intelligibility component may comprisea phoneme recognition component.

The method may further comprise outputting, by the audio fidelitysystem, a measure of similarity based on a difference between outputs ofthe phoneme recognition component for the input audio data and theoutput audio data of the voice morphing apparatus, and training thevoice morphing apparatus to minimize said difference.

The audio fidelity system may comprise an audio similarity component.The method may further comprise comparing, by the audio similaritycomponent, the input audio data and the output audio data of the voicemorphing apparatus, outputting, by the audio fidelity system, a measureof similarity based on an output of the audio similarity component andtraining the voice morphing apparatus to maximize an output of the audiosimilarity component for the input audio data and the output audio data.

The method may further comprise generating, by the audio similaritycomponent, a score indicative of a spectrogram similarity.

The method may further comprise applying one or more voice featureclassifiers to at least the output audio data for the voice morphingapparatus and using an output of the one or more voice featureclassifiers to adjust the set of trainable parameters for the voicemorphing apparatus. The method may further comprise comparing outputs ofthe one or more voice feature classifiers for the input audio data andthe output audio data of the voice morphing apparatus and using anoutput of the comparison to adjust the set of trainable parameters forthe voice morphing apparatus.

In accordance with another aspect, a voice morphing apparatus isprovided. The voice morphing apparatus may comprise a neural networkarchitecture to map input audio data to output audio data, the inputaudio data comprising a representation of speech from a speaker, theneural network architecture comprising a set of parameters, the set ofparameters being trained to reduce a speaker identification score fromthe input audio data to the output audio data and to optimize a speakerintelligibility score for the output audio data.

The voice morphing apparatus of this aspect may be used to morph speechin a manner that hides or masks a speaker identity. This may be usefulfor anonymizing speech data and/or for providing private voice queries.

In accordance with various aspects, the voice morphing apparatus maycomprise a noise filter to pre-process the input audio data, wherein thenoise filter is configured to remove a noise component from the inputaudio data and the voice morphing apparatus is configured to add thenoise component to output audio data from the neural networkarchitecture. This may enable noise to be isolated from the system toincrease a stability of training and/or preserve noise features of theaudio data for use as a subsequent speech data training set.

In accordance with various aspects, the neural network architecturecomprises one or more recurrent connections. For example, an output ofthe neural network architecture may be fed back as an input for futureoutputs, e.g. may form part of an input for a later time step.

In certain aspects, the voice morphing apparatus may be configured tooutput time-series audio waveform data based on the output audio datafrom the neural network architecture. In one case, the voice morphingapparatus may directly output time series audio data; in another case,the voice morphing apparatus may output spectrogram data that may beconverted to time series audio data.

In an aspect, a method for using a voice morphing apparatus is provided.The method comprises mapping, via a neural network architecture, inputaudio data to output audio data, the input audio data comprising arepresentation of speech from a speaker, the neural network architecturecomprising a set of parameters and training the set of parameters toreduce a speaker identification score from the input audio data to theoutput audio data and to optimize a speaker intelligibility score forthe output audio data.

The method may further comprise pre-processing the input audio data witha noise filter. The method may further comprise removing, by the noisefilter, a noise component from the input audio data and adding, by thevoice morphing apparatus, the noise component to output audio data fromthe neural network architecture. The neural network architecture maycomprise one or more recurrent connections.

The method may further comprise outputting, by the voice morphingapparatus, time-series audio waveform data based on the output audiodata from the neural network architecture.

According to another aspect, a non-transitory computer-readable storagemedium may be provided that stores instructions which, when executed byat least one processor, cause the at least one processor to: load inputaudio data from a data source; input the input audio data to a voicemorphing apparatus, the voice morphing apparatus comprising a set oftrainable parameters; process the input audio data using the voicemorphing apparatus to generate morphed audio data; apply a speakeridentification system to at least the morphed audio data to output ameasure of speaker identification; apply an audio fidelity system to themorphed audio data and the input audio data to output a measure of audiofidelity; evaluate an objective function based on the measure of speakeridentification and the measure of audio fidelity; and adjust the set oftrainable parameters for the voice morphing apparatus based on agradient of the objective function, wherein the objective function isconfigured to adjust the set of trainable parameters to optimize themeasure of audio fidelity between the morphed audio data and the inputaudio data and to modify the measure of speaker identification.

According to another aspect, there is provided a method for training avoice morphing apparatus. The method comprises loading input audio datafrom a data source, inputting the input audio data to the voice morphingapparatus, the voice morphing apparatus comprising a set of trainableparameters, processing the input audio data using the voice morphingapparatus to generate morphed audio data, applying a speakeridentification system to at least the morphed audio data to output ameasure of speaker identification, applying an audio fidelity system tothe morphed audio data and the input audio data to output a measure ofaudio fidelity, evaluating an objective function based on the measure ofspeaker identification and the measure of audio fidelity; and adjustingthe set of trainable parameters for the voice morphing apparatus basedon a gradient of the objective function, wherein the objective functionis configured to adjust the set of trainable parameters to optimize themeasure of audio fidelity between the morphed audio data and the inputaudio data and to modify the measure of speaker identification.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration showing a system for training a voicemorphing apparatus according to an embodiment and an aspect of theinvention.

FIG. 2 is a schematic illustration showing components of a trainingsystem according to an embodiment and an aspect of the invention.

FIG. 3 is a schematic illustration showing computation of a measure ofspeaker identification according to an embodiment and an aspect of theinvention.

FIG. 4 is a schematic illustration showing computation of a measure ofspeaker intelligibility according to an embodiment and an aspect of theinvention.

FIG. 5 is a schematic illustration showing computation of a measure ofaudio similarity according to an embodiment and an aspect of theinvention.

FIG. 6 is a schematic illustration showing computation of a parameterupdate using a loss function according to an embodiment and an aspect ofthe invention.

FIG. 7 is a schematic illustration showing components for computing avoice modification metric according to an embodiment and an aspect ofthe invention.

FIG. 8 is a schematic illustration showing computation of a measure ofaudio fidelity according to an embodiment and an aspect of theinvention.

FIG. 9 is a schematic illustration showing a number of classificationsto determine objective function terms according to an embodiment and anaspect of the invention.

FIG. 10 is a schematic illustration showing a noise filter according toan embodiment and an aspect of the invention.

FIGS. 11A and 11B are schematic charts illustrating featuredistributions in embodiment and an aspect of the invention systems.

FIG. 12 is a flow diagram showing a method of training a voice morphingapparatus according to an embodiment and an aspect of the invention.

FIG. 13 is a flow diagram showing a method of training a voice morphingapparatus according to another embodiment and an aspect of theinvention.

FIG. 14 is a schematic diagram showing a non-transitorycomputer-readable storage medium according to an embodiment and anaspect of the invention.

FIG. 15 is a schematic diagram showing a server computing deviceaccording to an embodiment and an aspect of the invention.

FIG. 16 is a schematic diagram showing components of the servercomputing device of FIG. 15.

DETAILED DESCRIPTION Introduction

The following describes various embodiments of the present technologythat illustrate various interesting aspects. Generally, embodiments canuse the described aspects in any combination. All statements hereinreciting principles, aspects, and embodiments are intended to encompassboth structural and functional equivalents thereof. Additionally, it isintended that such equivalents include both currently known equivalentsand equivalents developed in the future, i.e., any elements developedthat perform the same function, regardless of structure.

It is noted that, as used herein, the singular forms “a,” “an” and “the”include plural referents unless the context clearly dictates otherwise.Reference throughout this specification to “one,” “an,” “certain,”“various,” and “cases”, “embodiments” or similar language means that aparticular aspect, feature, structure, or characteristic described inconnection with the embodiment is included in at least one embodiment.Thus, appearances of the phrases “in one case,” “in at least oneembodiment,” “in an embodiment,” “in certain cases,” and similarlanguage throughout this specification may, but do not necessarily, allrefer to the same embodiment or similar embodiments. Furthermore,aspects and embodiments described herein are merely by way of example,and should not be construed as limiting of the scope or spirit of theinvention as appreciated by those of ordinary skill in the art. Theinvention is effectively made or used in any embodiment that includesany novel aspect described herein. Furthermore, to the extent that theterms “including”, “includes”, “having”, “has”, “with”, or variantsthereof are used in either the detailed description and the claims, suchterms are intended to be inclusive in a similar manner to the term“comprising.” In embodiments showing multiple similar elements, such asstorage devices, even if using separate reference numerals, some suchembodiments may work with a single element filling the role of themultiple similar elements.

Certain embodiments described herein relate to training a voice morphingapparatus. A voice morphing apparatus comprises a device that takesinput audio data and generates modified output audio data. The audiodata may comprise raw waveforms, e.g. one or more channels of pressureor microphone membrane displacement measurements over time, and/orprocessed audio data, including frequency measurements and spectrograms.The voice morphing apparatus may operate upon a series of time steps togenerate output audio data with a plurality of samples over time. In onecase, the input audio data and the output audio data may have a commontime base, e.g. a sample of output audio data is generated for everysample of input audio data. In certain cases, the voice morphingapparatus may be configured to generate an output waveform that may beplayed as a sound recording; in other cases, a further component maytake output audio from the voice morphing apparatus, e.g. in the form offrequency or spectrogram samples, and generate an output waveform thatmay be rendered. The voice morphing apparatus may be applied online(e.g. to real-time speech capture) and/or offline (e.g. to batches ofpre-recorded speech segments). In certain cases, the voice morphingapparatus may be configured to use the output audio data to replace theinput audio data, e.g. modify an audio file in-place.

In embodiments described herein the voice morphing apparatus isconfigured to modify input audio data to morph a voice present in theaudio data. Morphing a voice may comprise changing one or more auralcharacteristics of the voice. In embodiments described herein, the voiceis morphed to hide an identity of a speaker, e.g. such that a particularvoice audible in the output audio data is not distinguishable as thesame voice audible in the input audio data. The audio data is processedby the voice morphing apparatus such that speech is minimally distortedby the morphing, e.g. such that a person and/or an automatic speechrecognition system may still successfully process the speech despite amorphed voice.

Training System for a Voice Morphing Apparatus

FIG. 1 shows an embodiment 100 of a voice morphing apparatus 110. Thevoice morphing apparatus 110 receives input audio data 120 and maps thisto output audio data 130. The input audio data 120 and output audio data130 may have the same or different audio formats. In one case, one ormore of the input audio data 120 and output audio data 130 comprise oneor more time samples of Pulse Code Modulation (PCM) digital audio (e.g.sampled and quantized analog audio amplitude measurements). In thiscase, the audio data may comprise time series measurements from one ormore audio capture devices (e.g., one or more microphones). For example,one or more channels of PCM data may be captured at a predefinedsampling rate (e.g., 8 kHz or 44.1 kHz), where each sample isrepresented by a predefined number of bits (e.g., 8, 16 or 24 bits persample—where each sample comprise an integer or float value). In anothercase, one or more of the input audio data 120 and output audio data 130comprise processed audio data. Processing may comprise, e.g., filteringin one or more of the time and frequency domains, applying beam formingand noise reduction, and/or filtering and normalization. In one case,one or more of the input audio data 120 and output audio data 130 maycomprise measurements over time in the frequency domain, e.g., byperforming the (Fast) Fourier Transform, and/or one or more filter bankoutputs, e.g. filter banks may be applied to determine values for one ormore frequency domain features, such as Mel-Frequency CepstralCoefficients. In a certain case, one or more of the input audio data 120and output audio data 130 may comprise one or more frames of spectrogramdata, e.g. two-dimensional data that extends over time and a measurementdomain (such as the frequency domain or Mel values). Spectrograms may belikened to an image of a small window of a sound recording. It should benoted that various forms of audio data may be used, and that audio datamay be converted between various representations using various knownaudio processing components. Audio data as described herein may relateto any measurement made along an audio processing pipeline.

In FIG. 1, the voice morphing apparatus 110 is shown communicativelycoupled to a training system 140. The training system 140 adjustsparameters of the voice morphing apparatus 110 so as to provide adesired voice morphing functionality. In FIG. 1, the training system 140receives the input audio data 120 and the output audio data 130 and usesthis to train the voice morphing apparatus 110. In one case, the voicemorphing apparatus 110 may comprise an artificial neural networkarchitecture. In this case, the parameters of the voice morphingapparatus 110 may comprise values for one or more of weights and biasesfor one or more layers of the artificial neural network architecture. Inanother case, the voice morphing apparatus 110 may comprise aprobabilistic model, including one or more of a Hidden Markov Model anda Gaussian Mixture Model, wherein the parameters comprise parameters ofthe probabilistic model, such as probability distribution parameters. Inboth cases, the training system 140 may be configured to perform anoptimization procedure to optimize the parameter values. The trainingsystem 140 is configured to train the voice morphing apparatus 110 usinga plurality of training samples, e.g. a plurality of different sets ofinput audio data 120. A training sample may comprise a segment of audio,e.g. where the segment of audio comprises a plurality of time samples.The segment of audio may comprise a voice query or short voice segment.In FIG. 1, the training system 140 provides a mechanism to train thevoice morphing apparatus 110 in an unsupervised manner, as the inputaudio data 120 does not need labels indicating a particular “groundtruth” classification. The plurality of training samples may thuscomprise a large database of unlabeled speech samples.

FIG. 2 shows an embodiment 200 that illustrates a set of components thatmay form the training system 140. As in FIG. 1, the training system 140receives input audio data 120 and output audio data 130, the latterresulting from an application of the voice morphing apparatus 110. Thetraining system 140 of FIG. 2 comprises a speaker identification system210, an audio fidelity system 220 and an objective function evaluator230. In this embodiment, the speaker identification system 210 receivesthe output audio data 130. In later embodiments, it is also shown howthe speaker identification system 210 may also receive the input audiodata 120.

The speaker identification system 210 is configured to process at leastthe output audio data 130 to determine a measure of speakeridentification. This measure of speaker identification may comprise oneor more confidence values. In one case, the measure of speakeridentification may comprise a probability indicating whether the speakeridentification system 210 can successfully identify a speaker. Forexample, a value of 0.5 may indicate that the speaker identificationsystem 210 has a confidence of 50% in an identification of a speakerfeatured in the output audio data 130. Or put another way, a value of0.5 may indicate that a highest probability for a speaker classification(e.g. a maximum likelihood value) is 50%, e.g. the most likely speakeris speaker X who has a probability value of 50%. Different methods maybe used to generate the measure of speaker identification as long as themeasure is output within a predefined range (e.g. a normalized range of0 to 1 or an 8-bit integer value between 0 and 255). The output of thespeaker identification system 210 may comprise a normalized scalarvalue. In one case, the speaker identification system 210 may apply ahierarchical identification, e.g. perform a first identification todetermine a set of speakers and then perform a second identification todetermine a speaker within the determined set. In this case, the measureof speaker identification may comprise a probability from the secondidentification or an aggregate value (e.g. an average) across the set ofhierarchical stages.

The audio fidelity system 220, in the embodiment of FIG. 2, isconfigured to process the input audio data 120 and the output audio data130 to determine a measure of fidelity between the input audio data 120and the output audio data 130. The measure of fidelity may represent ameasure of the similarity of the audio inputs to the audio fidelitysystem 220. The term “fidelity” is used herein to represent a measure ofthe exactness or faithfulness of replication of a copy of an audiosignal to a comparative audio signal. The form of “audio fidelity” beinganalyzed by the audio fidelity system 220 is fidelity of all features ofa voice signal that do not make a speaker's identity distinguishable bythe sound of voice. This can include preservation of noise and othernon-speech sounds. In FIG. 2, this is determined by comparing the inputaudio data 120 and the output audio data 130. In other embodiments, themeasure of fidelity may be generated based on the output audio data 130alone, e.g. if the input audio data 130 is presumed to represent voiceaudio data, a comparison may be made with a generalized model of voiceaudio data instead of the specific input audio data 130. The audiofidelity system 220 may also output a normalized scalar value in asimilar manner to the speaker identification system 210.

In FIG. 2, the outputs of the speaker identification system 210 and theaudio fidelity system 220 are received by the objective functionevaluator 230. The objective function evaluator 230 is configured toevaluate an objective function that comprises a function of the outputsof the speaker identification system 210 and the audio fidelity system220. For example, the objective function may comprise a first term thatis based on the output of the speaker identification system 210 and asecond term that is based on the output of the audio fidelity system220. The term “objective function” is used as per the art of modeloptimization. The objective function may comprise a function of a set oftrainable parameters for the voice morphing apparatus 110. The objectivefunction may thus be evaluated to optimize the function by adjusting thetrainable parameters. An objective function may be optimized byattempting to maximize or minimize a value of the function. Bothmaximization and minimization may have the same effect, depending on howthe terms are presented (e.g. a function to be maximized may beconverted to a minimization problem via inversion). In a case, where theobjective function is to be minimized, it may be referred to as a costfunction. A loss function may form part of the cost function and maycomprise a function applied to each individual training sample or datapoint during training, e.g. in a minimization example, the objectivefunction may comprise a loss function that has the first term and thesecond term and that is applied to the input audio data 120 and theoutput audio data 130.

As shown in FIG. 1, an output of the objective function evaluator 230may be used to adjust the parameters of the voice morphing apparatus110. Different optimization methods may be used to adjust the parametersof the voice morphing apparatus 110. A common optimization method isgradient descent. In this case, the objective function evaluator 230 maybe configured to evaluate the objective function by determining aderivative of the objective function with respect to the parameters ofthe voice morphing apparatus 110. The derivative may then be used todetermine a gradient towards an extremum of the objective function. Inone case, the objective function evaluator 230 determines values for thegradient of the objective function and uses these to update theparameters of the voice morphing apparatus 110, e.g. the parameters maybe updated so as to modify the parameters in the “downwards” directionof the gradient towards a local minima. Different gradient descentmethods may be applied as known in the art, including stochasticgradient descent or batch/mini-batch gradient descent. Differentgradient descent optimization approaches may also be used, such as Adamor RMSProp. In multi-layer neural networks, the chain rule may be usedto determine the derivative across the set of layers of the neuralnetwork architecture.

By applying the components and systems shown in FIGS. 1 and 2, theparameters of the voice morphing apparatus 110 may be adjusted followingtraining over a plurality of training samples. In each trainingiteration, input audio data 120 and output audio data 130 may be used toevaluate the objective function, which may comprise evaluating a lossfunction. The objective function has terms that relate to both speakeridentification and audio fidelity. The speaker identification term inthe objective function acts to adjust the parameters to reduce a speakeridentification, e.g. to make it harder for the speaker identificationsystem 210 to identify the speaker. The audio fidelity term in theobjective function acts to adjust the parameters to maintain an audiofidelity, e.g. to keep the output audio data 130 sounding like a normalvoice. By applying both terms together, the parameters may be adjustedto achieve both aims. The voice morphing apparatus 110 is thusconfigured to generate output audio data 130 that allows for a speakerto be de-identified yet still allows the voice data to be processed byspeech processing systems and understood by human listeners.

In certain embodiments, the training system 140 may be implemented usingmachine learning libraries such as TensorFlow or PyTorch. Theselibraries provide interfaces for defining neural network architecturesand for performing training. These libraries allow for custom lossdefinitions and these may be used to implement the custom objectivefunctions described herein. In these cases, a derivative of theobjective function may be determined automatically using the methods ofthe libraries, e.g. by using the chain rule and automaticdifferentiation along a compute graph.

Re-Use of Speech Processing Components

In certain embodiments, one or more of the speaker identification system210 and the audio fidelity system 220 may comprise existing componentsof an automatic speech recognition system.

The speaker identification system 210 may comprise a component or modulein a speech processing pipeline that identifies a speaker. The speakeridentification system 210 may comprise a Hidden Markov Model and/orGaussian Mixture Model system for speaker identification or a neuralnetwork architecture for speaker identification, e.g. such as a systembased on x-vectors as described in the paper by Snyder, David, et al.“X-vectors: Robust DNN embeddings for speaker recognition.” 2018 IEEEInternational Conference on Acoustics, Speech and Signal Processing(ICASSP). IEEE, 2018 (the contents of which are incorporated herein byreference). In the case that the speaker identification system 210comprises a neural network architecture, the parameters of the speakeridentification system 210 may be fixed when training the voice morphingapparatus 110 (i.e. the parameters of the speaker identification system210 are not trained when training the voice morphing apparatus 110).

The audio fidelity system 220 may also comprise one or more audioprocessing components or modules of an automatic speech recognitionsystem. In one case, the audio fidelity system 220 may comprise aphoneme recognition system or acoustic model. This may again be aprobabilistic model or a neural network architecture. In one case, theaudio fidelity system 220 may comprise an acoustic model that receivesat least the output audio data 130 and determines a confidence orprobability vector for a set of available phones, phonemes and/orgraphemes. Like the speaker identification system 210 described above,an output of the audio fidelity system 220 may comprise a function ofthis confidence or probability vector. However, unlike the output of thespeaker identification system 210, in this case it is desired tomaximize the values of the confidence or probability vector, e.g. tohave a strong positive identification of linguistic features such asphonemes within the output audio data 130. As above, in the case thatthe audio fidelity system 220 comprises one or more neural networkarchitectures, the parameters of the audio fidelity system 220 may befixed when training the voice morphing apparatus 110 (i.e. theparameters of the audio fidelity system 220 are not trained whentraining the voice morphing apparatus 110). As the parameters of the twosystems are fixed, they may be treated as constants in any automaticdifferentiation of the objective function.

The present embodiments thus provide for a form of adversarial trainingof the voice morphing apparatus 110 using existing components of anautomatic speech recognition system or related speech processingtechnologies. This makes the training system 140 easy to implement, asexisting computer program code and/or hardware devices may be applied ina modular manner to build the training system 140 and output data foruse in evaluating an objective function for the voice morphing apparatus110. One or more of the speaker identification system 210 and the audiofidelity system 220 may comprise front-end components of an automaticspeech recognition system, such that a full speech processing pipelinedoes not need to be applied to train the voice morphing apparatus 110.

Comparative Systems

FIGS. 3 to 5 show embodiments that may be used in certain cases toimplement the speaker identification system 210 and the audio fidelitysystem 220 of FIG. 2. These embodiments operate based on a comparisonbetween outputs generated based on each of the input audio data 120 andthe output audio data 130. In other embodiments, e.g. as describedabove, only the output audio data 130 may be used (this is described ina later embodiment).

FIG. 3 shows an embodiment whereby a common speaker identificationsystem 310 is applied independently to both the input audio data 120 andthe output audio data 130. FIG. 3 shows two instances of the speakeridentification system 310-A and 310-B. These may represent, e.g., twocalls to a common Application Programming Interface (API) with differentdata, use of two parallel hardware processing units and/or use of acommon hardware processing unit at two different points in time. In onecase, the two instances of the speaker identification system 310-A and310-B may be applied in parallel to the input audio data 120 and theoutput audio data 130 to speed up training (e.g. using two parallelhardware device or two parallel processor threads executing a common setof computer program code). Outputs from the two instances of the speakeridentification system 310-A and 310-B are passed to a comparator 320.The first instance of the speaker identification system 310-A computes afirst speaker identification vector for the input audio data 120 and thesecond instance of the speaker identification system 310-B computes asecond speaker identification vector for the output audio data 120.These scores may comprise confidence or probability values as discussedabove. The comparator 320 compares the first and second speakeridentification vector to output a combined speaker identification scoreS_(ID) based on distance between the vectors that may be used by theobjective function evaluator 230. In one case, the speakeridentification systems 310 may output a probability vector across a setof speakers. In this case, the comparator 320 may evaluate a function ofthe two scores, such as a Euclidian distance or other distance measure.In the case, where the speaker identification systems 310 output ascalar score value, the comparator 320 may subtract the output of thesecond instance of the speaker identification system 310-B from thefirst instance of the speaker identification system 310-A. It may bedesired to maximize, in a loss function, a distance between the secondspeaker identification vector and the first speaker identificationvector, where this distance indicates a measure of speakerde-identification. Although the speaker identification system 310 isdeemed to output a score in the present embodiment, in other cases adifferent form of output may be processed by the comparator 320 todetermine a measure of speaker identification.

FIG. 4 shows a similar setup for the certain components of the audiofidelity system 220. In FIG. 4, the audio fidelity system 220 comprisesa speaker intelligibility system 410. This may comprise a phonemerecognition component or acoustic model as described above. The speakerintelligibility system 410 is configured to output a score indicative ofthe intelligibility of speech present within input audio data.Intelligibility may be determined in one or more ways. In one case,intelligibility may indicate a confidence or probability of detectingone or more linguistic features such as phonemes. In another case,intelligibility may be based on an accuracy or confidence of outputtranscribed text or voice commands. As set out above, the speakerintelligibility system 410 may comprise part of a speech processingpipeline for automatic speech recognition. In this case, an actual textoutput may be ignored but a confidence value that is provided as part ofthe output may be used. In other cases, the text output may be used.

As in FIG. 3, FIG. 4 shows two instances of the speaker intelligibilitysystem 410. The first instance of the speaker intelligibility system410-A computes a first speaker intelligibility score for the input audiodata 120 and the second instance of the speaker intelligibility system410-B computes a second speaker intelligibility score for the outputaudio data 120. The comparator 420 then compares these two scores togenerate a combined speaker intelligibility score S_(IG). In one case,the combined speaker intelligibility score S_(IG) may be based on achange in intelligibility between the input audio data 120 and theoutput audio data 130. In any loss function, it may be desired tominimize the change in intelligibility.

FIG. 5 shows an embodiment 500 that uses another component that may formpart of the audio fidelity system 220. This component comprises an audiocomparison system 510. The audio comparison system 510 receives andcompares the input audio data 120 and the output audio data 130. Theaudio comparison system 510 may differ from the speaker intelligibilitysystem 410 by comparing the audio data itself rather than a result ofperforming speech processing on the data. In one case, the audiocomparison system 510 may comprise a difference between spectrograms ofthe audio data, e.g. frequency, Mel or bark spectrograms. In certaincases, the audio comparison system 510 may only compare frequencymagnitude features as opposed to frequency phase features, as humanhearing is relatively insensitive to phase. The audio comparison system510 computes an audio comparison score S_(AC). The audio comparisonscore may comprise a distance metric, e.g. a sum of squares ofdifference over one or more components of the spectrogram or similarmethod of comparison. Non-speech features of audio signals such astransient and constant noises have a generally neutral effect on speakeridentification but work against speech intelligibility. Training with anobjective function that only considers speaker identification and speechintelligibility components would result in the morphing apparatus 110learning to remove non-speech features. Training with a loss functionthat includes an audio comparison term S_(AC) causes the morphingapparatus 110 to better preserve audio signal features.

FIG. 6 shows an embodiment 600 wherein the objective function comprisesa loss function 610 to be minimized. In this case, the loss function 610takes as input the three scores from FIGS. 3 to 5. If the speakeridentification score S_(ID) comprises a distance measurement then it maybe desired to maximize this score; if the speaker intelligibility scoreS_(IG) and the audio comparison score S_(AC) also comprise distancemeasurements then it may be desired to minimize these latter scores. Assuch, in the loss function, a negative weight (e.g. a=−1*positivescalar) may be applied to the speaker identification score S_(ID) andpositive weights applied to the speaker intelligibility score S_(IG) andthe audio comparison score S_(AC); in this manner, training may seek tominimize the loss function which may in turn attempt to maximize aspeaker identification distance. In FIG. 6, the loss function isevaluated to determine gradient terms 630 that are used to modify theparameters of the voice morphing apparatus 110. In the loss function,the speaker identification score S_(ID) is used in a first term and thespeaker intelligibility score S_(IG) and the audio comparison scoreS_(AC) are used in a composite second, audio fidelity, term.

Those skilled in the art will understand that there may be manydifferent ways to construct an objective or loss function withcomparative functionality. For example, the comparator 320 may outputthe speaker identification score S_(ID) as an inverse of a distancemeasure between speaker identification probability vectors, in whichcase a positive weight may be applied such that minimizing this termmaximizes the distance. The scores may be determined per time samples ormay be averaged over a plurality of time samples.

In one case, weights for each score may be predetermined, e.g. so as togive more importance to one or more of the scores. In one case, thescores and/or the weight may be normalized, e.g. such that the weightssum to one and the scores are a value between 0 and 1. In other cases,the weights may comprise parameters that are optimized as part of thetraining. In yet other cases, the weights may be dynamic and changebased on the scores and/or other information associated with the inputaudio data 120.

Alternative Training Systems

FIG. 7 shows an alternative embodiment 700 of components of the trainingsystem 140. In FIG. 7, only the output audio data 130 is used within theobjective function (e.g. is passed to the objective function evaluator230).

FIG. 7 shows a speaker identification system 710 and a speakerintelligibility system 720 that each receive the output audio data 130.The speaker identification system 710 and the speaker intelligibilitysystem 720 may be similar to the corresponding systems of FIGS. 3 and 4.In this case, both systems may be configured to output a confidencescore indicative of a confidence in detection of respectiveidentification and intelligibility features. For example, the speakeridentification system 710 may output a score indicating a confidence inidentifying a speaker present in the output audio data 130 and thespeaker intelligibility system 720 may output a score indicating aconfidence in decoding linguistic features present in the output audiodata 130. As per previous embodiments, the score may comprise part of anavailable output of existing automatic speech recognition components. InFIG. 7, the two scores are respectively weighted by weights a and b andare aggregated by aggregator 730. In one case, the aggregator 730 maysubtract the output of the speaker identification system 710 from theoutput of the speaker intelligibility system 720. In another case, theaggregator 730 may compute a ratio of the outputs of the speakeridentification system 710 and the speaker intelligibility system 720. Ifthe output of the aggregator 730 is to be minimized this may comprise afunction of the speaker identification system output divided by thespeaker intelligibility system output, such that modification of theparameters of the voice morphing apparatus 110 reduce the output of thespeaker identification system 710 and maximize the output of the speakerintelligibility system 720.

FIG. 8 shows another alternative embodiment 800 that may be used toimplement the audio fidelity system 220 in FIG. 2. In FIG. 8, the audiofidelity system 220 comprises a spectrogram difference component 810 anda phoneme recognition component 820. Both components receive the inputaudio data 120 and the output audio data 130 as inputs (dashed lineshave been used for the input audio data 120 for clarity). Thespectrogram difference component 810 outputs a spectrogram differencescore S_(SD) that indicates an audio distance between spectrograms ofthe input audio data 120 and the output audio data 130. Thesespectrograms may comprise Mel spectrograms or bark spectrograms and thespectrogram difference score may comprise a fidelity distance such as asum of squares of difference over components of the spectrogram. Thephoneme recognition component 820 outputs a phoneme recognition scoreS_(PR) that indicates a measure of speech intelligibility. The outputmay indicate a change in speech intelligibility between the input audiodata 120 and the output audio data 130, e.g. based on linguistic featureprobabilities output by an acoustic model. The scores may be determinedper time samples or may be averaged over a plurality of time samples. Incertain cases, the phoneme recognition score S_(PR) may be based on adifference between the highest and second highest phoneme probability,e.g. measures of how the phoneme probability distributions change due tomorphing. In FIG. 8, an audio fidelity function 830 receives thespectrogram difference score S_(SD) and the phoneme recognition scoreS_(PR) and combines them into a suitable audio fidelity score S_(AF)that is suitable for use in the objective function. In one case, theaudio fidelity function 830 may also combine individual scores over timesamples to generate a score for a duration of the input and output audiodata 120, 130.

In the embodiments, including those of FIGS. 7 and 8, a measure ofspeech intelligibility is used as this may help reduce the voicemorphing apparatus 110 being trained to simply garble speech in a mannerthat is difficult for humans and speech recognition systems to process.By attempting to minimize successful speaker identification togetherwith maintaining a measure of speech intelligibility (in certain casesas well minimizing a difference in audio features) trains the voicemorphing apparatus 110 to produce useful audio output including speechand other features such as transient and constant noise.

Additional Classifiers

FIG. 9 shows an embodiment 900 wherein additional factors may beincluded in the objective function. In FIG. 9, one or more classifiers910 are included to generate additional scores (e.g. in addition tothose shown in FIGS. 3 to 5, 7 and 8) that may be used to train thevoice morphing apparatus 110 to preserve (or remove) certain voicefeatures. In FIG. 9, n classifiers 910 are shown. Each classifierreceives the input audio data 120 and the output audio data 130. Inother embodiments, only the output audio data 130 may be used.

In FIG. 9, each classifier 910 is configured to perform aclassification, and compare outputs for the input audio data 120 and theoutput audio data 130, to generate a classifier score S_(Ci) that may beused in the objective function. The classifiers may relate to one ormore voice characteristics such as gender and accent. To train the voicemorphing apparatus 110 to preserve a particular voice characteristic(e.g. to keep a female voice female), each classifier score mayrepresent a difference between a common classification applied to boththe input audio data 120 and the output audio data 130. For example, theclassifier score may be determined as a mean-squared difference betweenclassification probability vectors for each of the input audio data 120and the output audio data 130. Again, if the classifiers comprise neuralnetwork architectures their weights are fixed for the training of thevoice morphing apparatus 110. The classifiers may also comprise existingcomponents of an automatic speech recognition pipeline. If thedifference is large, then this will show up in the classifier score anda resultant loss function; hence, parameters of the voice morphingapparatus 110 will be adjusted to minimize the distance betweenclassifications and as such preserve the voice features.

In one case, different classifiers may be added or removed in a modularmanner to configure the voice morphing apparatus 110 and/or to generatedifferent instances of the voice morphing apparatus 110 that preserve orchange different characteristics. In one case, for each feature that isto be changed (“flipped”), a term may be added to a loss function suchthat, when the loss function is minimized, the difference between aclassifier for the feature applied to the input audio data and aclassifier for the feature applied to the output audio data ismaximized. For example, this may be achieved by using an inverse of thedifference between the classifiers for the feature in the loss function.

Noise Filters

FIG. 10 shows an embodiment 1000 where a noise filter 1010 is used withthe voice morphing apparatus 110. In this embodiment 1000, otherelements of the training system 140 have been omitted for clarity andjust the input and output audio generation is shown. The noise filter1010 is configured to receive the input audio data 120 prior toprocessing by the voice morphing apparatus 110. The noise filter 1010 isarranged to pre-process the input audio data 120. In particular, thenoise filter 1010 is arranged to remove a noise component n from theinput audio data. This leaves modified input audio data I′ that ispassed to the voice morphing apparatus 110. The voice morphing apparatus110 is configured to output audio data O as before. However, in thiscase, the voice morphing apparatus 110 is trained to map filtered inputaudio data. It may thus generate a different output audio data than theprevious embodiments. In FIG. 10, a summation component 1015 is used toadd the noise component n extracted by the noise filter 1010 to theaudio data output O by the voice morphing apparatus 110. This thengenerates modified output audio data 1030 (O′) that contains theextracted noise component.

The embodiment of FIG. 10 may be used to improve training by allowingthe voice morphing apparatus 110 to concentrate on learning mappings foraudio features particular to voice as opposed to also learning how tomap noise components. This may be useful for real-world audio samples,such as those obtained from within vehicles or on mobile computingdevices, which may have a heavy noise component. In cases where thevoice morphing apparatus 110 is a pre-processing step for the generationof training data for an automatic speech recognition system, it may bedesired to keep the noise characteristics of the original input audiodata 120 as this may be required for robust training. FIG. 10 provides amechanism where this may be achieved.

In the embodiment of FIG. 10, the noise component n may be any componentof the original audio signal that is not used for speakeridentification. Put another way, the modified input audio data I′ mayonly comprise information that a speaker identification system needs toidentify a speaker. This may not resemble a conventional view (or auralimpression) of “speech”. In this manner, the filtering and recombiningof the noise component n may actually also filter and recombine portionsof speech that are not used by the speaker identification system. Thismay help recognition and fidelity.

Voice Morphing Apparatus

In certain cases, the voice morphing apparatus described herein may bebased on a so-called neural vocoder, i.e. a neural network architecturecomprising encoder and decoder components. In certain cases, the neuralnetwork architectures may only implement a “vocoder decoder” part of atraditional vocoder, e.g. that maps processed audio features into outputaudio data that may comprise a time-series waveform. When comparing witha traditional vocoder, the “vocoder encoder” part of the neural vocodermay not need to be implemented using a neural network architecture, butinstead may be implemented using conventional audio signal processingoperations (e.g. the Fast Fourier Transform—FFT—and/or filter banks,taking the magnitude and/or logarithm). In this case, the “vocoderencoder” part of the neural vocoder may not be “neural” but may comprisethe audio pre-processing operations described herein. Only the “vocoderdecoder” portion of these architectures may comprise a neural networkarchitecture with a set of trainable parameters.

It should also be noted that the neural network architecture maycomprise a neural encoder-decoder (e.g. autoencoder-like) architectureas considered from the neural network perspective. This may or may notmap onto the traditional encoder-decoder portions of a traditional(non-neural) vocoder. For example, a “vocoder decoder” portion of avocoder may be implemented using a neural encoder-decoder architecture.

The neural vocoder may comprise one or more recurrent connections. Thesemay not be needed in all embodiments, e.g. convolutional neural networkarchitectures may alternatively use a plurality of frames of audio dataincluding frames before a current frame and frames ahead of a currentframe. These approaches may be able to use a sliding window so as toavoid slower recurrent connections (such as found within recurrentneural networks). In one case, the voice morphing apparatus isconfigured to receive time-series audio waveform data and outputtime-series audio waveform data; in other cases, the audio data maycomprise frequency or Mel features as described. The neural vocoder maycomprise one or more convolutional neural network layers and/or one ormore feedforward neural network layers. Embodiments of suitable neuralvocoder architectures that may be used as a basis for the voice morphingapparatus 110 include those described in “Efficient Neural AudioSynthesis” by Kalchbrenner et al. (published via arXiv on 25 Jun. 2018),“Waveglow: A Flow-Based Generative Network For Speech Synthesis” byPrenger et al. (published via arXiv on 31 Oct. 2018) and “TowardsAchieving Robust Universal Neural Vocoding” by Lorenzo-Trueba at al.(published via arXiv on 4 Jul. 2019), all of which are incorporatedherein by reference.

Data Distributions

In certain embodiments, the plurality of input audio data 120 ispre-selected to provide a defined distribution of voice characteristics.For example, it may be beneficial to train the voice morphing apparatusdescribed herein on a large data set of voice recordings that feature adiverse range of voices. It may also be recommended to use a large dataset of diverse voice content, e.g. a plurality of different phrases asopposed to many different voices repeating a common phrase (such as awake word).

In certain embodiments, a large range of training samples (e.g. for useas input audio data 120) may be generated or augmented using parametricspeech synthesis. In this case, speech samples may be generated byselecting the parameters of the speech synthesis system. For example, atraining set may be generated by creating random (or pseudo random) textsegments and then using a text-to-speech system to convert the text toaudio data. In this case, the parameters of the text-to-speech systemmay also be randomly sampled (e.g. random or pseudo random selectionsusing inbuilt software library and/or hardware functions) to generate adiverse set of training samples. For example, to ensure diversity, anarray of speech synthesis parameter sets can be learned that is able tocreate speech from text, where the speech has an even distribution ofvectors matching a range defined by vectors computed from speech from abroad range of human voices within an embedding space.

In certain cases, a speaker identification system may itself by trainedon a database of audio data from a plurality of different speakers. Thespeakers that are used to train the speaker identification system mayaffect the training of the voice morphing apparatus (e.g. when theparameters of the speaker identification system are fixed and are usedto train the apparatus in an adversarial manner). For example, in onecase, the training method described herein may act to modify the inputaudio data so as to change a distribution of features that are used forspeaker identification, e.g. as may be present in one or more hidden oroutput layers of a neural speaker identification system. FIG. 11A showsa schematic “toy” example of two possible distributions for aone-dimensional feature for speaker identification. A top distribution1110 has an even distribution of data points whereas a bottomdistribution 1120 has a clustered distribution of data points. Whentraining the voice morphing apparatus, the voice morphing apparatus maylearn to modify the input audio data such that the features of the audiodata that are used for speaker identification are moved into the spacebetween data points. For a clustered distribution such as 1120 this maycomprise a space between groups of data points. How the voice morphingapparatus modifies the input audio data may thus be controlled bycontrolling for the distribution of data samples in the training data.This may be performed in a pre-processing step that applies the speakeridentification system and determines a feature distribution for speakeridentification for each data sample. Data samples may then be selectedbased on these feature distributions. If the data samples are selectedto have a diverse set of feature distributions then the voice morphingapparatus may be able to make more stable small modifications to theinput audio data that still mask the speaker but that avoid largeunpredictable modifications to the input audio data. On the other hand,clustered features may be preferred if it is desired to obfuscate orremove those features, e.g. to remove accent characteristics the speakeridentification system may be trained upon recordings that all featurethick accents, such that when the voice morphing apparatus is trainedusing this speaker identification system, the apparatus learns to jumpaway from the cluster of accent features.

Certain embodiments described herein differ from comparative approachesthat attempt to map speaker features present in input audio data toeither another target speaker or an average of a set of target speakers.These comparative approaches suffer from issues, such as instead ofanonymizing a voice, they instead assign the voice to another speaker.This may lead to its own privacy issues. In certain embodimentsdescribed herein, however, the voice morphing apparatus is trained torepel speaker features present in the input audio from known speakeridentification speakers, effectively making it difficult to determine anidentity as opposed to swapping an identity. This may be shown in theexample chart 1130 of FIG. 11B. This example shows a two-dimensional“toy” speaker feature vector. The black circles 1140 represent datapoints for known speakers (e.g. that represent voices on which thespeaker identification system was trained). The black cross 1150 showsan example feature vector for a new input audio sample, e.g. featuringan unknown speaker. By applying the methods of the various aspects andembodiments of the invention, the voice morphing apparatus learns to mapthe example feature vector to the feature vector shown by a white cross1160, e.g. into empty feature space in a manner that makesidentification difficult (e.g. the white cross 1160 is distant from theblack circles 1140 and the black cross 1150). In effect, the white cross1160 shows a mapped feature vector as seen by the speaker identificationsystem that has a maximal distance from existing speaker feature vectorsmaking the morphed audio data difficult to identify. In comparativesystems, the black cross 1150 is typically mapped onto another of theblack circles 1140. The arrows 1170 in FIG. 11B also show the action ofthe audio fidelity term. This acts to constrain the voice morphingapparatus to avoid it morphing the input audio data to result in speakerfeature vectors that are simply outside of a training set (e.g. atextreme points as would be found with random noise or distorted speech).The audio fidelity term in effect repels from the extremes within thespeaker vector space. Of course, in real examples, the vector space mayhave hundreds of dimensions as opposed to the one or two shown in thesimple examples of FIGS. 11A and 11B.

In certain embodiments, to optimize the parameters of the voice morphingapparatus such that they de-identify a voice in a manner suitable forhuman listeners, it may be preferred that the speaker identificationsystem is optimized such that a profile of their relative accuracyacross training voices is as close as possible to a profile of humanlisteners' relative accuracy across the same voices. Hence, when tryingto minimize a speaker identification certainty, the voice morphingapparatus will learn to modify the voice in the input audio data in amanner that minimizes the change in audio features but that maximizesconfusion for human beings. It is preferred to have a large diverse setof voice characteristics such that the voice morphing apparatus may makeminimal changes to the input audio data. For example, if the speakeridentification is trained using a plurality of people with a thickaccent, it may learn to adjust the voice within the feature space of thethick accent but in a manner that results in a voice with a thick accentthat is not identifiable.

In certain cases, it may be possible to train the voice morphingapparatus using audio data from a single speaker. In this case, aspeaker identification system may be trained on many speakers (which mayinclude the speaker). However, improved morphing characteristics may bepresent when the voice morphing apparatus is trained using audio datafrom multiple speakers that are distributed evenly in voice featurespace. Multiple speakers may work to reduce noise and randomness (e.g.jumps in the gradient) when training and improve convergence. In onecase, mini-batches may be used to average out differences acrossmultiple speakers and/or normalization may be applied. One form ofnormalization may use speaker embeddings. For example, a training setmay indicate a speaker identification (e.g. an ID number) that may beused to retrieve an embedding (i.e. a vector of values) that representsthe speaker. The speaker embeddings may be trained with the whole system(and/or components of the system). If speaker embeddings are provided asan input during training, the voice morphing apparatus may be able touse this information to learn to normalize voices without averaging outspecific information about different regions of voice feature space.

Methods

FIG. 12 shows, in accordance with some aspects of the invention, aprocess or method 1200 of training a voice morphing apparatus accordingto an embodiment. The voice morphing apparatus may comprise a voicemorphing apparatus as described with reference to any of the previousembodiments. The method of training may comprise applying the trainingsystem 140 of FIG. 1 or any of the components described with referenceto the other embodiments.

At block 1205, the method 1200 comprises evaluating an objectivefunction for a plurality of data samples. Each data sample may be usedto generate an input-output pair, e.g. based on input audio datatraining samples, where the output audio data is generated using thevoice morphing apparatus. The objective function is defined as afunction of at least an output of the voice morphing apparatus, wherethis output is generated based on a corresponding input, e.g. asreceived as a training sample. The objective function may comprise aloss function applied to each training sample, where the loss functionis to be minimized. In other embodiments, the objective function maycomprise a function to be optimized, e.g. by locating an extremum suchas a minimum or maximum.

The objective function comprises a first term based on speakeridentification and a second term based on audio fidelity. For example,the first term may be based on a measure of speaker identificationdetermined using at least the output of the voice morphing apparatus.For example, this measure of speaker identification may comprise theoutput of the one of the speaker identification systems 210, 310 or 710.It may be computed using an output of a speaker identification componentand may comprise a certainty or confidence score. The first termmodifies the objective function in proportion to the measure of speakeridentification, e.g. may increase a value of a loss function to beminimized as a certainty or confidence of identification increases ormay decrease a value of an objective function to be maximized. If themeasure of speaker identification comprises an identification distance,e.g. a measure of a difference between a speaker probability vectordetermined based on the input audio data and a speaker probabilityvector determined based on the output audio data, then the first termmay decrease a value of a loss function in proportion to this distance(such that the loss function is minimized as the distance is maximized).

The second term modifies the objective function proportional to ameasure of audio fidelity between the output and the input. In certaincases, this may be based on both the input and the output; in othercases, it may be based on the output alone. The measure of audiofidelity may be a measure output by one or more of the components 220,410, 510, 720 and 810 to 830. If the measure of audio fidelity comprisesa distance measure, then an objective function to be minimized may bemodified proportional to this measure (such that the objective functionis minimized as the distance is minimized); if the measure of audiofidelity comprise a linguistic feature recognition score or probability,then an objective function to be minimized may be modified proportionalto an inverse or negatively weighted version of this measure (such thatthe loss function is minimized as the linguistic feature recognitionscore is maximized). The term “proportional” is used in the embodimentsherein in a broad sense to mean “based on”, “in accordance with” or “asa function of”. In the objective function itself, terms may be based onpositive and/or negative weights, and/or may be modified using inversecomputations depending on the measures that are used. The term “measure”is also used broadly herein to cover one or more of continuous values,discrete values, scalars, vectors (and other multidimensional measures),categorical values, and binary values (amongst others).

At block 1210, the evaluating at block 1205 is used to adjust parametersof the voice morphing apparatus. For example, if the voice morphingapparatus comprises an artificial neural network architecture, thenadjusting parameters of the voice morphing apparatus comprises applyinga gradient descent method to a derivative of the objective function withrespect to the parameters of the artificial neural network architecture.The dashed line in FIG. 12 indicates that blocks 1205 and 1210 may beiterated over a plurality of training samples to train the voicemorphing apparatus, e.g. the blocks may be repeated for one or moretraining epochs comprising one or more batches or mini-batches where theevaluating of the objective function is performed.

FIG. 13 shows, in accordance with some aspects and other embodiments ofthe invention, a process or method 1300 of training a voice morphingapparatus. The method of FIG. 13 may be applied when the voice morphingapparatus comprises an artificial neural network architecture, such as aversion of the neural vocoders described above. At block 1305 inputaudio data is obtained. This may comprise loading a sample of audio datafrom a training set comprising a plurality of input speech segments. Atblock 1310, the voice morphing apparatus is used to generate morphedaudio data. Block 1310 may be performed using a current set ofparameters values, e.g. prior to adjustment. At block 1315, the morphedaudio data generated at block 1310 is used to obtain a speakeridentification score. The speaker identification score may comprise aconfidence in correctly identifying a speaker using the morphed audiodata. At block 1320, which may be performed in parallel with block 1315,the morphed audio data generated at block 1310 is used to obtain anaudio fidelity score. The audio fidelity score may comprise at least aconfidence of correctly determining a number of linguistic features fromthe morphed audio data. Blocks 1315 and 1320 may correspond todetermining values for first and second terms of the objective functionin block 1305. At block 1325, a gradient of a loss function isdetermined. The loss function is a function of the speakeridentification score and the audio fidelity scores. The loss functionmay increase in proportion to an increase of the speaker identificationscore and decrease in proportion to an increase in the audio fidelityscore. The gradient may be based on a derivative with respect totrainable parameters of the voice morphing apparatus. At block 1330, thetrainable parameters of the voice morphing apparatus are adjusted usingthe gradient of the loss function. For example, an adjustment to theparameters may be made to follow the gradient towards a minimum tooptimize the loss function. Block 1330 may be performed for individualtraining samples and/or as part of a batch update (e.g. when usingstochastic gradient descent to modify the parameters). As per FIG. 12,the method 1300 may be repeated over a plurality of training samples asshown by the dotted line.

In certain embodiments, obtaining an audio fidelity score at block 1320,or evaluating the objective function at block 1205, may comprisecomputing a first phoneme recognition score for the input to the voicemorphing apparatus using an audio processing component and computing asecond phoneme recognition score for the output from the voice morphingapparatus using the audio processing component. The second term of theobjective function, or the audio fidelity score, may be evaluated basedon a comparison between the first and second phoneme recognition scores,e.g. representing a phoneme recognition distance. For example, this isalso demonstrated in the embodiment of FIGS. 4 and 8.

In certain embodiments, obtaining an audio fidelity score at block 1320,or evaluating the objective function at block 1205, may alternatively oradditionally comprise comparing a spectrogram for the input to the voicemorphing apparatus and a spectrogram for the output of the voicemorphing apparatus. In this case, the second term of the objectivefunction, or the audio fidelity score, may be evaluated based on thecomparison. For example, this is also demonstrated in the embodiment ofFIGS. 5 and 8.

In certain embodiments, obtaining a speaker identification score atblock 1315, or evaluating the objective function at block 1205, maycomprise computing a first speaker identification vector for the inputto the voice morphing apparatus using a speaker identification componentand computing a second speaker identification vector for the output fromthe voice morphing apparatus using the speaker identification component.The first term of the objective function, or the speaker identificationscore, may be evaluated based on a distance between the first and secondspeaker identification vectors, e.g. representing a speakeridentification distance. For example, this is also demonstrated in theembodiment of FIG. 3.

In certain embodiments, the objective function evaluated at block 1205of the method 1200 comprises one or more further terms based on one ormore of a gender classification using at least the output of the voicemorphing apparatus and an accent classification using at least theoutput of the voice morphing apparatus, wherein the one or more furtherterms are weighted to either maintain or move away from one or more of agender classification and an accent classification. For example, thismay comprise modifying the method 1300 of FIG. 13 to also use theclassifiers shown in FIG. 9. In this case, the one or more further termsmay be based on the output audio data alone or a comparative scorebetween a classification applied to the input of the voice morphingapparatus and a classification applied to the output of the voicemorphing apparatus.

In these methods, an objective function, such as a loss function, maycombine a speaker identification certainty measure with an inverse of anaudio fidelity distance. The combination of two or more terms may be aweighted sum of each term. In certain cases, the weights may also belearned during training as a trainable parameter of the voice morphingapparatus. In certain cases, the weights may be dynamic, and may changebased on values of one or more of the terms. For example, in one casethe weights within the loss function may be applied as a form ofattention layer during training. The speaker identification score ormeasure may be a vector. In certain cases, each element of this vectormay relate to a different speaker identification feature and/or adifferent speaker to be identified. The audio fidelity score or measuremay also comprise a vector. In certain cases, each element of thisvector may relate to a frequency band, Mel feature and/or other audiofeature. In these cases, the measures of speaker identification and/oraudio fidelity may be distance measures within the multi-dimensionalspace of the vectors.

It should be noted that in embodiments described herein, the speakeridentification measure or data and the audio fidelity measure or datamay comprise one or more of continuous and discrete representations. Forexample, using a logit or probability output from a speakeridentification system or an audio fidelity component may provide for arelatively continuous representation (within the limits of the precisionof the number representation), which may result in a smooth andcontinuous loss function that may facilitate training. In other cases,however, the voice morphing apparatus may be trained as part of agenerative adversarial network (GAN) and/or using a game-theory basedalgorithm. In these latter cases, discrete representations such ascategorical data may be used as the measure or data. For example, themeasure may be a speaker ID and/or a binary measure indicatingsuccessful identification or unsuccessful identification. Usingdifferential approaches, as described herein, may help to filter outinconsistencies (e.g. like a cough in the input audio data) and may helpavoid disrupting “jumps” (i.e. discontinuities) in the gradient.

Certain embodiments described herein may enable a neural network basedvoice morphing apparatus to be trained for a combination of at leastthree objectives: changing the sound of the voice of any speech;preserving the output audio as closely as possible to the input audio;and preserving the intelligibility of speech. In certain embodiments,the voice morphing apparatus may be trained adversarially with respectto at least a speaker identification system. This may be achieved byusing a training loss function for the voice morphing apparatus thatpenalizes a high certainty or confidence from the speaker identificationsystem.

In certain embodiments, to reduce a risk that the voice morphingapparatus simply learns to output random noise, an objective functionmay be defined that includes a first term that is dependent on thespeaker identification certainty and a second term that is dependent onan audio fidelity. If the objective function comprises a loss functionto be minimized, then the loss function may comprise a loss term orelement that is positively weighted based on the speaker identificationcertainty and a loss term or element that is negatively (or inversely)weighted based on a distance score between the input and output audiodata. A speaker identification term alone would tend to learn a mappingto random noise, wherein an audio fidelity term alone would tend tolearn to copy the input to the output (e.g. as a simple pass throughfilter). However, a combined loss function, where each loss term isappropriate configured to steer the loss of the training, yields a voicemorphing apparatus that anonymizes a user yet maintains features ofspeech that may be understood by a human or a machine and preservesnon-speech audio features such as transient or constant noise. thedistance score from an input to output audio signal fidelity distancemodel.

The systems and methods of training described herein also enable certainnon-identifying features of speech audio, such as noise, gender, andaccent to be preserved. For example, this may be achieved by addingadditional loss function terms based on classifier outputs, e.g. asdescribed with reference to FIG. 9, or by isolating certain noisesources, as described with reference to FIG. 10. In these cases, afemale speaker with a Californian accent may be morphed such that thegender and accent are still recognizable but such that other audiofeatures are moved away from the particular characteristics of the inputspeaker, e.g. to a more neutral or general female Californian speakerthat masks the identity of the original speaker.

Computer Readable Medium

FIG. 14 shows an embodiment 1400 of a non-transitory computer-readablestorage medium 1410 storing instructions 1420 that, when executed by atleast one processor 1430, cause the at least one processor to perform amethod of training a voice morphing apparatus. This method of trainingmay be similar to the method described with reference to FIGS. 12 and 13and may be implemented using the systems of any of the otherembodiments.

At block 1432, the processor is instructed to load input audio data froma data source. The data source may be internal or external. The inputaudio data may comprise the input audio data 120 of FIG. 1. At block1434, the processor is instructed to input the input audio data to avoice morphing apparatus. The voice morphing apparatus may comprise thevoice morphing apparatus 110 of the previous embodiments. At block 1436,the processor is instructed to process the input audio data using thevoice morphing apparatus to generate morphed audio data. The morphedaudio data may comprise the output audio data 130 of FIG. 1. At block1438, the processor is instructed to apply a speaker identificationsystem to at least the morphed audio data to output a measure of speakeridentification. The speaker identification system may comprise acomponent part of an automatic speech recognition system. The measure ofspeaker identification may comprise a certainty or confidence score, ora distance measure between identification characteristics of the inputand morphed audio data. At block 1440, the processor is instructed toapply an audio fidelity system to the morphed audio data and the inputaudio data to output a measure of audio fidelity. The audio fidelitysystem may comprise one or more component parts of an automatic speechrecognition system. The measure of audio fidelity may comprise asimilarity or distance measure that compares the audio and/orintelligibility characteristics of the input and morphed audio data. Atblock 1442, the processor is instructed to evaluate an objectivefunction based on the measure of speaker identification and the measureof audio fidelity. This may comprise evaluating a derivative of theobjective function and using gradient descent to optimize the objectivefunction. At block 1444, the processor is instructed to adjust a set oftrainable parameters for the voice morphing apparatus based on agradient of the objective function. For example, the trainableparameters may be adjusted by a small amount in a direction that seeksto maximize or minimize the objective function. In general, theobjective function is configured to adjust the set of trainableparameters to optimize the measure of audio fidelity between the morphedaudio data and the input audio data and to modify the measure of speakeridentification, e.g. to reduce a confidence or certainty of successfulspeaker identification.

Server Implementations

FIG. 1500 shows a rack-mounted server blade multi-processor serversystem 1500 that may be used to implement the systems and/or perform themethods of the described embodiments. It comprises a multiplicity ofnetwork-connected computer processors that run software in parallel.

FIG. 16 shows a block diagram of the server system 1500. It comprises amulticore cluster of computer processor (CPU) cores 1610 and optionallya multicore cluster of graphics processor (GPU) cores 1620. Theprocessors connect through a board-level interconnect 1630 torandom-access memory (RAM) devices 1640 for program code and datastorage. Server system 1500 also comprises a network interface 1650 toallow the processors to access a network such as a local area network(LAN) or the Internet. By executing instructions stored in RAM devices1640 through interface 1630, the CPUs 1610 and/or GPUs 1620 performsteps of methods as described herein.

Implementations

Certain embodiments described herein may be applied to speech processingincluding automatic speech recognition. The voice morphing apparatus,once trained, may be used as part of a speech processing pipeline, e.g.a selectively applicable anonymizer that may offer users a “private”speech mode. The voice morphing apparatus may be used to enhance privacyand anonymize the labelling of training data by removing recognizablecomponents.

Certain methods and sets of operations as described herein may beperformed by instructions that are stored upon a non-transitory computerreadable medium. The non-transitory computer readable medium stores codecomprising instructions that, if executed by one or more computers,would cause the computer to perform steps of methods described herein.The non-transitory computer readable medium may comprise one or more ofa rotating magnetic disk, a rotating optical disk, a flash random accessmemory (RAM) chip, and other mechanically moving or solid-state storagemedia.

Certain embodiments have been described herein, and it will be notedthat different combinations of different components from differentembodiments may be possible. Salient features are presented to betterexplain embodiments; however, it is clear that certain features may beadded, modified and/or omitted without modifying the functional aspectsof these embodiments as described.

Various embodiments are methods that use the behavior of either or acombination of humans and machines. Method embodiments are completewherever in the world most constituent steps occur. Some embodiments areone or more non-transitory computer readable media arranged to storesuch instructions for methods described herein. Whatever machine holdsnon-transitory computer readable media comprising any of the necessarycode may implement an embodiment. Some embodiments may be implementedas: physical devices such as semiconductor chips; hardware descriptionlanguage representations of the logical or functional behavior of suchdevices; and one or more non-transitory computer readable media arrangedto store such hardware description language representations.Descriptions herein reciting principles, aspects, and embodimentsencompass both structural and functional equivalents thereof.

Practitioners skilled in the art will recognize many possiblemodifications and variations. The modifications and variations includeany relevant combination of the disclosed features. Descriptions hereinreciting principles, aspects, and embodiments encompass both structuraland functional equivalents thereof. Elements described herein as“coupled” or “communicatively coupled” have an effectual relationshiprealizable by a direct connection or indirect connection, which uses oneor more other intervening elements. Embodiments described herein as“communicating” or “in communication with” another device, module, orelements include any form of communication or link. For example, acommunication link may be established using a wired connection, wirelessprotocols, near-field protocols, or RFID.

The scope of the invention, therefore, is not intended to be limited tothe embodiments shown and described herein. Rather, the scope and spiritof present invention is embodied by the appended claims.

1. A voice morphing apparatus comprising: a neural network architectureto map input audio data to output audio data, the input audio datacomprising a representation of speech from a speaker, the neural networkarchitecture including a set of parameters, the set of parameters beingtrained to reduce a speaker identification score from the input audiodata to the output audio data and to optimize a speaker intelligibilityscore for the output audio data.
 2. The voice morphing apparatus ofclaim 1 further comprising a noise filter to pre-process the input audiodata.
 3. The voice morphing apparatus of claim 2, wherein the noisefilter removes a noise component from the input audio data and the voicemorphing apparatus adds the noise component to output audio data fromthe neural network architecture.
 4. The voice morphing apparatus of anyof claim 1, wherein the neural network architecture comprises one ormore recurrent connections.
 5. The voice morphing apparatus of any ofclaim 1, wherein the voice morphing apparatus is configured to outputtime-series audio waveform data based on the output audio data from theneural network architecture.
 6. A non-transitory computer-readablestorage medium for storing instructions that, when executed by at leastone processor, cause the at least one processor to: load input audiodata from a data source; input the input audio data to a voice morphingapparatus, the voice morphing apparatus including a set of trainableparameters; process the input audio data using the voice morphingapparatus to generate morphed audio data; apply a speaker identificationsystem to at least the morphed audio data to output a measure of speakeridentification; apply an audio fidelity system to the morphed audio dataand the input audio data to output a measure of audio fidelity; evaluatean objective function based on the measure of speaker identification andthe measure of audio fidelity; and adjust the set of trainableparameters for the voice morphing apparatus based on a gradient of theobjective function, wherein the objective function is configured toadjust the set of trainable parameters to optimize the measure of audiofidelity between the morphed audio data and the input audio data and tomodify the measure of speaker identification.
 7. A method for optimizingtraining parameters, the method comprising: loading input audio datafrom a data source; inputting the input audio data to a voice morphingapparatus, the voice morphing apparatus including a set of trainableparameters; processing the input audio data using the voice morphingapparatus to generate morphed audio data; applying a speakeridentification system to at least the morphed audio data to output ameasure of speaker identification; applying an audio fidelity system tothe morphed audio data and the input audio data to output a measure ofaudio fidelity; evaluating an objective function based on the measure ofspeaker identification and the measure of audio fidelity; and adjustingthe set of trainable parameters for the voice morphing apparatus basedon a gradient of the objective function, wherein the objective functionis configured to adjust the set of trainable parameters to optimize themeasure of audio fidelity between the morphed audio data and the inputaudio data and to modify the measure of speaker identification.